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import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle


# Read in an image and grayscale it
image = mpimg.imread('signs_vehicles_xygrad.png')

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import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle


# Read in an image
image = mpimg.imread('signs_vehicles_xygrad.png')

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import cv2
import matplotlib 

matplotlib.image.imread() #RGB image,
cv2.imread() # BGR image.

hls = cv2.cvtColor(im, cv2.COLOR_RGB2HLS) #HLS

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import matplotlib.pyplot as plt
f, (ax1, ax2) = plt.subplots(ncols=1, nrows=2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(gradx, cmap='gray')
ax1.set_title('Gradx', fontsize=50)
ax2.imshow(grady, cmap='gray')
ax2.set_title('Grady', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
f.savefig('grads.png')   # save the figure to file
plt.close(f) 

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import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
from skimage.feature import hog

# Read in our vehicles and non-vehicles
images = glob.glob('*.jpeg')
cars = []

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import argparse

arg_parser = argparse.ArgumentParser()
arg_parser.add_argument("-p", "--path", required=True, help="Path of the files")
args = vars(arg_parser.parse_args())

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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(boston_housing.data, boston_housing.target, test_size=0.2, random_state=0)
model = LinearRegression()
model.fit(X_train, y_train)

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from pydantic import BaseModel

class HousingFeatures(BaseModel):
    CRIM: float
    ZN: float
    INDUS: float
    CHAS: float
    NOX: float
    RM: float
    AGE: float

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from fastapi import APIRouter, Depends
api = APIRouter()
@api.post("/predict", response_model=PredictionResult)
def post_predict(
    housing_features: HousingFeatures,
    authenticated: bool = Depends(security.validate_request),
):
    assert authenticated == True
    return predict(housing_features)

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import onnxruntime as rt
import numpy
sess = rt.InferenceSession("boston_housting.onnx")
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
prediction = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]